KDD'15 Chairs and Organizing Committee
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Halifax, Nova Scotia Canada August 13 17, 2017
Halifax, Nova Scotia Canada August 13 17, 2017 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining Contents KDD 2017 Agenda at a Glance KDD 2017 Chairs’ Welcome Message Program Highlights Keynote Talks Research and Applied Data Science Tracks Applied Data Science Track Invited Talks Applied Data Science Panels Tutorials Hands‐On Tutorials Workshops KDD 2017 Tutorial Program KDD 2017 Workshop Program Full Day Workshops ‐ Monday August 14, 8:00am ‐5:00pm Half Day Workshops ‐ Monday August 14, 8:00am ‐ 12:00pm Half Day Workshops ‐ Monday August 14, 1:00am ‐ 5:00pm KDD Cup Workshop ‐ Wednesday August 16, 1:30pm ‐ 5:00pm KDD 2017 Hands‐On Tutorial Program Tuesday August 15, 2017 Wednesday August 16, 2017 Thursday August 17, 2017 KDD 2017 Conference Program Monday August 14 2017 Detailed Program Monday August 14, 2017 5:15pm – 7:00pm, KDD 2017 Opening Session ‐ Scoabank Centre Tuesday August 15, 2017 Detailed Program Wednesday August 16, 2017 Detailed Program Thursday August 17, 2017 Detailed Program KDD 2017 Conference Organizaon KDD 2017 Organizing Commiee Research Track Senior Program Commiee Applied Data Science Track Senior Program Commiee Research Track Program Commiee Applied Data Science Track Program Commiee KDD 2017 Sponsors & Supporters Halifax, Points of Interest Useful Links and Emergency Contacts KDD 2017 Agenda at a Glance Saturday, August 12th Level 8 Summit 8:00AM 5:00PM Workshop: Broadening Participation in Data Mining (BPDM) Day 1 Suite/Meeting Room 5 4:00PM 6:00PM KDD 2017 Registration Level 1 Atrium -
EDTM2021-Programbook.Pdf
Welcome Message from Chairs The General and TPC Chairs cordially welcome you to the 2021 IEEE Electron Devices Technology and Manufacturing (EDTM) Conference, to be held in Chengdu, China, during April 8-11, 2021. Sponsored by IEEE Electron Devices Society (EDS), EDTM is a premier conference providing a unique forum for discussions on a broad range of device/manufacturing-related topics. EDTM rotates among the hot-hubs of semiconductor manufacturing in Asia. The 5th EDTM is coming to China in 2021 for the first time. Come to EDTM2021 to learn from renowned researchers and engineers from around the globe through a rich list of technical sessions, interactive sessions, tutorials and short courses, and industrial exhibits. Share your knowledge and latest results with peers, and enjoy networking by meeting old friends and making new friends. Return invigorated with new ideas and enthusiasm to make new impacts. EDTM2021 highlights: Keynotes: EDTM2021 features plenary keynotes from globally recognized scholars and researchers from both the academia and the industry, including Dr. Haijun Zhao, co-CEO of Semiconductor Manufacturing International Corporation (SMIC), talking about alternative foundry innovation strategies; Professor Xiang Zhang, President of University of Hong Kong, describing photonics enabling future hi-resolution cameras ; Teruo Hirayama, Executive Chief Engineer of Sony Corporation, reviewing technical innovations for image sensors; Professor Arokia Nathan from University of Cambridge, discussing about thin-film transistors for advanced analog signal processing; Prof. Ru Huang, Vice President of Peking University, offering a review on advances in ferroelectric- based devices; and Dr. Jeff Xu, Director of HiSilicon Research, looking into future semiconductor technology driven by ubiquitous computing. -
Big Graph Analytics Platforms
Full text available at: http://dx.doi.org/10.1561/1900000056 Big Graph Analytics Platforms Da Yan The University of Alabama at Birmingham [email protected] Yingyi Bu Couchbase, Inc. [email protected] Yuanyuan Tian IBM Almaden Research Center, USA [email protected] Amol Deshpande University of Maryland [email protected] Boston — Delft Full text available at: http://dx.doi.org/10.1561/1900000056 Foundations and Trends R in Databases Published, sold and distributed by: now Publishers Inc. PO Box 1024 Hanover, MA 02339 United States Tel. +1-781-985-4510 www.nowpublishers.com [email protected] Outside North America: now Publishers Inc. PO Box 179 2600 AD Delft The Netherlands Tel. +31-6-51115274 The preferred citation for this publication is D. Yan, Y. Bu, Y. Tian, and A. Deshpande. Big Graph Analytics Platforms. Foundations and Trends R in Databases, vol. 7, no. 1-2, pp. 1–195, 2015. R This Foundations and Trends issue was typeset in LATEX using a class file designed by Neal Parikh. Printed on acid-free paper. ISBN: 978-1-68083-242-6 c 2017 D. Yan, Y. Bu, Y. Tian, and A. Deshpande All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, mechanical, photocopying, recording or otherwise, without prior written permission of the publishers. Photocopying. In the USA: This journal is registered at the Copyright Clearance Cen- ter, Inc., 222 Rosewood Drive, Danvers, MA 01923. Authorization to photocopy items for internal or personal use, or the internal or personal use of specific clients, is granted by now Publishers Inc for users registered with the Copyright Clearance Center (CCC). -
An Introduction to Deep Reinforcement Learning Full Text Available At
Full text available at: http://dx.doi.org/10.1561/2200000071 An Introduction to Deep Reinforcement Learning Full text available at: http://dx.doi.org/10.1561/2200000071 Other titles in Foundations and Trends® in Machine Learning Non-convex Optimization for Machine Learningy Prateek Jain and Purushottam Ka ISBN: 978-1-68083-368-3 Kernel Mean Embedding of Distributions: A Review and Beyond Krikamol Muandet, Kenji Fukumizu, Bharath Sriperumbudur and Bernhard Scholkopf ISBN: 978-1-68083-288-4 Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions Andrzej Cichocki, Anh-Huy Phan, Qibin Zhao, Namgil Lee, Ivan Oseledets, Masashi Sugiyama and Danilo P. Mandic ISBN: 978-1-68083-222-8 Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives Andrzej Cichocki, Anh-Huy Phan, Qibin Zhao, Namgil Lee, Ivan Oseledets, Masashi Sugiyama and Danilo P. Mandic ISBN: 978-1-68083-276-1 Patterns of Scalable Bayesian Inference Elaine Angelino, Matthew James Johnson and Ryan P. Adams ISBN: 978-1-68083-218-1 Generalized Low Rank Models Madeleine Udell, Corinne Horn, Reza Zadeh and Stephen Boyd ISBN: 978-1-68083-140-5 Full text available at: http://dx.doi.org/10.1561/2200000071 An Introduction to Deep Reinforcement Learning Vincent François-Lavet Peter Henderson McGill University McGill University [email protected] [email protected] Riashat Islam Marc G. Bellemare McGill University Google Brain [email protected] [email protected] Joelle Pineau Facebook, McGill University [email protected] Boston — Delft Full text available at: http://dx.doi.org/10.1561/2200000071 Foundations and Trends® in Machine Learning Published, sold and distributed by: now Publishers Inc. -
MMDS 2014: Workshop on Algorithms for Modern Massive Data Sets
MMDS 2014: Workshop on Algorithms for Modern Massive Data Sets Stanley Hall University of California at Berkeley June 17{20, 2014 The 2014 Workshop on Algorithms for Modern Massive Data Sets (MMDS 2014) will address algorithmic and statistical challenges in modern large-scale data analysis. The goals of MMDS 2014 are to explore novel techniques for modeling and analyzing massive, high-dimensional, and nonlinearly-structured scientific and internet data sets; and to bring together computer scientists, statisticians, mathematicians, and data analysis practitioners to promote the cross-fertilization of ideas. Organizers: Michael Mahoney, Alexander Shkolnik Petros Drineas, Reza Zadeh, and Fernando Perez Workshop Schedule Tuesday, June 17, 2014: Data Analysis and Statistical Data Analysis Time Event Location/Page Registration & Opening Lobby outside Stanley Hall 8:00{9:45am Breakfast and registration 9:45{10:00am Organizers Welcome and opening remarks First Session Stanley Hall 10:00{11:00am Ashok Srivastava, Verizon pp. 13 Large Scale Machine Learning at Verizon (Tutorial) 11:00{11:30am Dan Suciu, University of Washington pp. 14 Communication Cost in Big Data Processing 11:30{12:00pm Gerald Friedland, ICSI pp. 9 Content-based search in 50TB of consumer-produced videos 12:00{2:00pm Lunch (on your own) Second Session Stanley Hall 2:00{2:30pm Bill Howe, University of Washington pp. 10 Myria: Scalable Analytics as a Service 2:30{3:00pm Devavrat Shah, MIT pp. 13 Computing stationary distribution, locally 3:00{3:30pm Yiannis Koutis, University of Puerto Rico pp. 12 Spectral algorithms for graph mining and analysis 3:30{4:00pm Jiashun Jin, Carnegie Mellon University pp. -
Program Book
2016 China-US Joint Symposium International Nexus of Food, Energy, Water, and Soil Introduction of Conference China-US Symposium on Environmental Science and Pollution Control (International Nexus of Food, Energy, Water, and Soil) October 27-29, 2016 Table of Content Introduction of Conference……………..…………….……..…..…....1 Introduction of Partnerships………………..………………………...4 Organizers and Committee…..…………………….……..………….14 Conference Venue…………………………………………………….17 Agenda………………………………………………………….……..18 Abstract………………………………………………………….….....28 List of Participants..………………………………………………….112 2016 China-US Joint Symposium International Nexus of Food, Energy, Water, and Soil Introduction of Conference The 2016China-US Joint Annual Symposium “International Nexus of Food, Energy, Water, and Soil” October 26-29, 2016, Yixing, Jiangsu Province, China “Celebrating the Tenth Anniversary of the China-US Joint Research Center” 2006─Build and Explore─2016 Keynote Speaker: Dr. Warren Washington, Awarded the 2010 U.S. National Medal of Science 1 2016 China-US Joint Symposium International Nexus of Food, Energy, Water, and Soil Introduction of Conference INTRODUCTION Climate change, urbanization, population growth, and accelerating consumption of energy and natural resources bring great challenges for regional and global sustainable development. It is becoming imperative how society can best integrate across the natural and built environments to provide a growing demand for food, energy and water while maintaining key ecosystem functions and services. The U.S. and China have the largest economies and drive the rates and patterns of global resource utilization, energy consumption, and environmental degradation. The US and China share responsibility for developing realistic goals, effective strategies, and practical protocols for the best solutions for global energy, climate, food, and environmental problems that transform the production and use of limited energy and ecosystem resources. -
Towards Effective and Efficient Learning at Scale
Effective and Efficient Learning at Scale Adams Wei Yu AUGUST 2019 CMU-ML-19-111 Machine Learning Department School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Thesis Committee: Jaime Carbonell, Co-Chair Alexander Smola, Co-Chair (Amazon) Ruslan Salakhutdinov Quoc Le (Google) Christopher Manning (Stanford) Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy. Copyright c 2019 Adams Wei Yu This research was supported by a grant from the Boeing Company, a fellowship from the Nvidia Corporation, a fellowship from the Snap Inc, a fellowship from the Thomas and Stacey Siebel Foundation and a Carnegie Mellon University presidential fellowship. Keywords: Large Scale Optmization, Deep Neural Networks, Machine Reading Compre- hension, Natural Language Processing To my beloved wife, Yanyu Long and son, Ziheng Yu. iv Abstract How to enable efficient and effective machine learning at scale has been a long- standing problem in modern artificial intelligence, which also motivates this thesis research. In particular, we aim at solving the following problems: 1. How to efficiently train a machine learning model? 2. How to speed up inference after the model is trained? 3. How to make the model generalize better? We approach those problems from two perspectives: models and algorithms. On one hand, we design novel models that are intrinsically fast to train and/or test. On the other, we develop new algorithms with rapid convergence guarantee. Not surprisingly, the above three problem are not mutually exclusive and thus solving one of them might also benefit others. For example, 1) a new model that can enable parallel computation helps accelerate both training and inference; 2) a fast algorithm can save time for hyper-parameter tuning and/or make it affordable for training with more data, which in return boosts the generalization performance. -
A New Beginning 56 56
SUMMER 2018 e 16th President and Vice-Chancellor PROFESSOR XIANG ZHANG A NEW BEGINNING Cover Story A New Beginning HKU President and Vice-Chancellor Professor Xiang Zhang pledged to take the University to new heights. “The best way to be ahead of the curve is to define the curve ourselves.” P.12 Fireside Chats @ HKU Foundation Chamber P.14 The Growing Network 220,000+ alumni | 131 alumni groups P.16 Research Matters Breakthroughs, awards & more Summer 2018 P. 22 iDendron | DreamCatchers P. 26 Making a Perfect 10 at the C9+1 Symposium P. 28 HKU-Zhejiang Institute of Research and Innovation (ZIRI) CONTENTS P. 30 Architecture at the Shanghai Study Centre (BSc (Surv) Year 4) 范家朗 P. 31 New Platform for BioMed Innovation in Guangdong P. 32 Honorary Graduates SUMMER 2018 56 Fan Anthony by Photos th Convocation Newsletter P.11 Cheering on HKU athletes at the 18 Asian Games （ 香 港 大 學 畢 業 生 議 會 通 訊 ） is a university magazine for alumni, teachers and friends. Advisory Committee: Professor Lee Chack-fan 李焯芬 (BSc(Eng) 1968; MSc(Eng) 1970) Professor Cheng Kai-ming 程介明 (BSc 1966; BSc(Sp) 1967; CertEd 1981; AdvDipEd 1982; MEd 1983) Professor Ying Chan 陳婉瑩 (BSocSc 1970) Mr Andrew Fung 馮可強 (BA 1969; DipEd 1972) Dr Lawrence Fung 馮紹波 (BSocSc 1972; Hon DSocSc 2010) Mr Man Cheuk-fei 文灼非 (BA 1987) Mr Poon Kai-tik 潘啟迪 (BA 1975) Managing Editor: Director Development & Alumni Affairs Office P. 38 Published by: A Guiding Voice Lives On Development & Alumni Affairs Office The University of Hong Kong The Ian Davies Professorship in Ethics Views expressed in the Newsletter do not necessarily represent those of the University or HKU Convocation. -
Generalized Low Rank Models
Full text available at: http://dx.doi.org/10.1561/2200000055 Generalized Low Rank Models Madeleine Udell Operations Research and Information Engineering Cornell University [email protected] Corinne Horn Electrical Engineering Stanford University [email protected] Reza Zadeh Computational and Mathematical Engineering Stanford University [email protected] Stephen Boyd Electrical Engineering Stanford University [email protected] Boston — Delft Full text available at: http://dx.doi.org/10.1561/2200000055 R Foundations and Trends in Machine Learning Published, sold and distributed by: now Publishers Inc. PO Box 1024 Hanover, MA 02339 United States Tel. +1-781-985-4510 www.nowpublishers.com [email protected] Outside North America: now Publishers Inc. PO Box 179 2600 AD Delft The Netherlands Tel. +31-6-51115274 The preferred citation for this publication is M. Udell, C. Horn, R. Zadeh and S. Boyd. Generalized Low Rank Models. Foundations and Trends R in Machine Learning, vol. 9, no. 1, pp. 1–118, 2016. R This Foundations and Trends issue was typeset in LATEX using a class file designed by Neal Parikh. Printed on acid-free paper. ISBN: 978-1-68083-141-2 c 2016 M. Udell, C. Horn, R. Zadeh and S. Boyd All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, mechanical, photocopying, recording or otherwise, without prior written permission of the publishers. Photocopying. In the USA: This journal is registered at the Copyright Clearance Cen- ter, Inc., 222 Rosewood Drive, Danvers, MA 01923. Authorization to photocopy items for internal or personal use, or the internal or personal use of specific clients, is granted by now Publishers Inc for users registered with the Copyright Clearance Center (CCC). -
REZA ZADEH [email protected] Reza-Zadeh.Com 650.898.3193
REZA ZADEH [email protected] reza-zadeh.com 650.898.3193 SUMMARY Founder CEO at Matroid, Adjunct Professor at Stanford University Served on the Technical Advisory board of Microsoft and Databricks Ph.D. from Stanford. Masters from Carnegie Mellon. Bachelors from Waterloo. Advanced research and engineering positions at Google, Twitter, IBM Focused on Machine Learning, Distributed Computing, and Discrete Applied Mathematics Contributor to Apache Spark and TensorFlow, initial creator of the Linear Algebra Package in Spark Wrote an O'Reilly Media book on TensorFlow, used at Matroid for Computer Vision Created and taught courses on distributed algorithms and discrete mathematics at Stanford Mentored undergraduate, masters, and Ph.D. students on research Sifted through petabytes of data by way of Google MapReduce and Apache Hadoop, Pig, Spark Published and refereed for Machine Learning conferences and journals Organized international conferences and workshops on Machine Learning Interactive resume at http://reza-zadeh.com. EDUCATION Ph.D. in Computational Mathematics Stanford University Advisor: Gunnar Carlsson April 2014 Thesis: Large Scale Graph Completion. Master of Computer Science Carnegie Mellon University August 2010 Fully funded research masters. Bachelor of Mathematics, Honors Computer Science, Software Engineering University of Waterloo August 2008 Active in undergraduate research and programming competitions. EXPERIENCE Matroid January 2016 - Current Palo Alto, California Chief Executive Officer • Making Computer Vision Simple. $15.5 -
Proceedings of SPIE
Experimental observation of chiral phonons in monolayer WSe2 Item Type Conference Paper Authors Zhu, Hanyu; Yi, Jun; Li, Ming-yang; Xiao, Jun; Zhang, Lifa; Yang, Chih-Wen; Yang, Sui; Kaindl, Robert A.; Li, Lain-Jong; Wang, Yuan; Zhang, Xiang Citation Zhu, H., Yi, J., Li, M.-Y., Xiao, J., Zhang, L., Yang, C.-W., … Zhang, X. (2019). Experimental observation of chiral phonons in monolayer WSe2. Ultrafast Phenomena and Nanophotonics XXIII. doi:10.1117/12.2510760 Eprint version Publisher's Version/PDF DOI 10.1117/12.2510760 Publisher SPIE-Intl Soc Optical Eng Rights Archived with thanks to Proceedings of SPIE Download date 23/09/2021 22:00:36 Link to Item http://hdl.handle.net/10754/656524 PROCEEDINGS OF SPIE SPIEDigitalLibrary.org/conference-proceedings-of-spie Experimental observation of chiral phonons in monolayer WSe2 Hanyu Zhu, Jun Yi, Ming-Yang Li, Jun Xiao, Lifa Zhang, et al. Hanyu Zhu, Jun Yi, Ming-Yang Li, Jun Xiao, Lifa Zhang, Chih-Wen Yang, Sui Yang, Robert A. Kaindl, Lain-Jong Li, Yuan Wang, Xiang Zhang, "Experimental observation of chiral phonons in monolayer WSe2," Proc. SPIE 10916, Ultrafast Phenomena and Nanophotonics XXIII, 109160C (27 February 2019); doi: 10.1117/12.2510760 Event: SPIE OPTO, 2019, San Francisco, California, United States Downloaded From: https://www.spiedigitallibrary.org/conference-proceedings-of-spie on 19 Aug 2019 Terms of Use: https://www.spiedigitallibrary.org/terms-of-use Invited Paper Experimental Observation of Chiral Phonons in Monolayer WSe2 Hanyu Zhua, b, Jun Yia, Ming-Yang Lic, Jun Xiaoa, Lifa Zhangd, Chih-Wen Yangc, Sui Yanga, Robert A. -
Declarative Machine Learning Systems
DECLARATIVE MACHINE LEARNING SYSTEMS Piero Molino Christopher Ré Department of Computer Science Department of Computer Science Stanford University Stanford University [email protected] [email protected] July 20, 2021 1 Introduction In the last twenty years machine learning (ML) has progressively moved from a academic endeavor to a pervasive technology adopted in almost every aspect of computing. ML-powered products are now embedded in every aspect of our digital lives: from recommendations of what to watch, to divining our search intent, to powering virtual assistants in consumer and enterprise settings. Moreover, recent successes in applying ML in natural sciences [Senior et al., 2020] had revealed that ML can be used to tackle some of the hardest real-world problems humanity faces today. For these reasons ML has become central in the strategy of tech companies and has gathered even more attention from academia than ever before. The process that led to the current ML-centric computing world was hastened by several factors, including hardware improvements that enabled the massively parallel processing , data infrastructure improvements that enabled storage and consumption of massive datasets needed to train most ML models, and algorithmic improvements that allowed better performance and scaling. Despite these successes, we argue that what we have witnessed in terms of ML adoption is only the tip of the iceberg. Right now the people training and using ML models are typically experienced developers with years of study working within large organizations, but we believe the next wave of ML systems will allow a substantially larger amount of people, potentially without any coding skills, to perform the same tasks.